The video below contains a complete 90 minute Socket.IO course using Python and JavaScript.

This is the list of chapters, each with a link to the code for each part of the tutorial:

Below you can watch my complete "Quick React Tutorial" video series. The entire seven-part series runs for about 93 minutes and will give you a fairly complete overview of the core features of the React framework.

Below you can see the list of parts in the series, each with a link to the code for that part of the tutorial:

When you learn Flask, you are told to create your Flask application instances by passing __name__ as the first argument to the Flask class. Most developers do this without thinking, and without knowing what it achieves.

In this article we are going to look at Flask(__name__) in depth. By the end you will not only have a full understanding of this pattern, but you will also know when to deviate from it and pass other values.

I'm often asked how to kill a background thread, and the answer to this question makes a lot of people unhappy: threads cannot be killed. In this article I'm going to show you two options we have in Python to terminate threads.

A couple of months ago there was an article titled Async Python is Not Faster making the rounds on social media. In it, the author Cal Paterson made the point that contrary to popular belief, asynchronous web frameworks are not only "not faster" than their traditional synchronous counterparts, but they are also slower. He supports this by showing the results of a fairly complete benchmark that he implemented.

I wish things were as simple as this author puts them in his blog post, but the fact is that measuring web application performance is incredibly complex and he's got a few things wrong, both in the implementation of the benchmark and in his interpretation of the results.

In this article you can see the results of my effort in understanding and fixing this benchmark, re-running it, and finally arriving at a shocking revelation.

Have you heard people say that async Python code is faster than "normal" (or sync) Python code? How can that be? In this article I'm going to try to explain what async is and how it differs from normal Python code.

This is the third article in my "React + Flask" series, in which I discuss applications that combine a Flask API server with a React single-page application. This time I'm going to show you how to work with the popular React-Router library for React, and in particular how this library affects the production deployment of the application.

This is the third article in my "React + Flask" series. Make sure you read the first and second parts, as this part builds on the project built up to this point.

In this beginner level video I explain what steps you need to take when you get an Internal Server Error in your Flask application.

If you've done any work with SQLite databases you surely know that this database is very limited in terms of making changes to the database schema. When working with a migration framework such as Flask-Migrate, it is common to end up with migration scripts that fail to upgrade or downgrade just because they need to remove or modify a column in a table, something that SQLite does not support.

In this article I'm going to discuss this limitation of the SQLite database, and show you a workaround that is specific to Flask-Migrate and Alembic.

A common need of web applications is to have a periodically running task in the background. This could be a task that imports new data from third party sources, or maybe one that removes revoked tokens from your database once they have expired. In this and many other situations you are faced with the challenge of implementing a task that runs in the background at regular intervals.

This is a pattern that many people ask me about. I've seen implementations that are based on the APScheduler package, on Celery, and even homegrown solutions built inside a background thread. Sadly none of these options are very good. In this article I'm going to show you what I believe is a very robust implementation that is based on the Flask CLI and the cron service.